Weipeng Cao

3.2k citations
67 papers · 2.4k · 1 hit paper · h-index 22

Impact in

Papers in

Weipeng Cao

60 papers receiving 2.3k citations

Weipeng Cao's Hit Papers

A review on neural networks with random weights 2017 · 357 citations
3570+3+6Years since publication100200300

Peers

Weipeng Cao
Comparison fields: 5 of 164
  • Biomaterials 389
  • Artificial Intelligence 497
  • Biomedical Engineering 503
  • Computer Vision and Pattern Recognition 228
  • Molecular Biology 741
Replace J. Manikandan with:
J. Manikandan India
Chunxi Liu China
Jingxian Wu United States
Jun Han China
Jing Gao China
Xinyang Zhang China
Haibao Wang China
Yonghong Song China
Yan Wang China
Xiang Li China
Weipeng Cao relative to J. Manikandan India J. Manikandan's profile →
Citations per field
00.5×2.7×
J. Manikandan · 1×
Citations per year

Countries citing papers authored by Weipeng Cao

Since Specialization
Citations

This map shows the geographic impact of Weipeng Cao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Weipeng Cao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weipeng Cao more than expected).

Fields of papers citing papers by Weipeng Cao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Weipeng Cao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Weipeng Cao. The network helps show where Weipeng Cao may publish in the future.

Co-authors

The 25 scholars most cited alongside Weipeng Cao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Weipeng Cao Line = papers co-authored together Weipeng Cao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 67 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A review on neural networks with random weights
Hit paper breakdown →
2017357
2 2010335
3 2011253
4 2012185
5 2012160
6 2013111
7 201378
8 201473
9 201465
10 200658
11 201252
12 201451
13 201548
14 202142
15 202041
16 201840
17 201536
18 201728
19 201727
20 202027

About Weipeng Cao

Weipeng Cao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Information Systems and Biomedical Engineering, having authored 67 papers that have together received 2.4k indexed citations. Recurring topics across this work include Machine Learning and ELM (21 papers), Domain Adaptation and Few-Shot Learning (17 papers), Neural Networks and Applications (12 papers), Multimodal Machine Learning Applications (9 papers), Face and Expression Recognition (8 papers), RNA Interference and Gene Delivery (5 papers), Advanced Neural Network Applications (5 papers) and Autophagy in Disease and Therapy (4 papers). The work is most often cited by research in Biomaterials (389 citations), Artificial Intelligence (497 citations), Biomedical Engineering (503 citations), Computer Vision and Pattern Recognition (228 citations) and Molecular Biology (741 citations). Weipeng Cao has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Xizhao Wang, Xing‐Jie Liang, Zhong Ming, Jinzhu Gao, Guozhang Zou, Xu Zhang, Ye‐Guang Chen, Chan Gao, Sha He and Yuran Huang. Their work appears in journals such as Engineering Applications of Artificial Intelligence, Neurocomputing, International Journal of Machine Learning and Cybernetics, Nanoscale and Journal of Controlled Release.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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